Joint audio-video facial animation system

ABSTRACT

The present invention relates to a joint automatic audio visual driven facial animation system that in some example embodiments includes a full scale state of the art Large Vocabulary Continuous Speech Recognition (LVCSR) with a strong language model for speech recognition and obtained phoneme alignment from the word lattice.

PRIORITY CLAIM

This application is a continuation of and claims the benefit of priorityto U.S. patent application Ser. No. 16/749,753, filed on Jan. 22, 2020,which is a continuation of and claims the benefit of priority to U.S.patent application Ser. No. 15/858,992, filed on Dec. 29, 2017, whichclaims the benefit of priority to U.S. Provisional Patent ApplicationSer. No. 62/577,548, filed on Oct. 26, 2017, the benefit of priority ofeach of which are claimed hereby, and each of which are incorporated byreference herein in their entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to mobilecomputing technology and, more particularly, but not by way oflimitation, to systems for tracking facial landmarks and generating a 3Dfacial model based on audio and video data.

BACKGROUND

Research has shown that facial tracking and performance capturingtechnology have had significant impacts in a broad range of fields thatinclude computer gaming, animations, entertainment, human-computerinterface. For example, some of the research has shown that usersinteracting with a digital avatar, such as an animated face, are 30%more trustworthy than compared with the same interactions with text-onlyscripts.

Existing facial animation systems follow one of two techniques:performance-based facial animation; or speech-driven facial animation.Performance-based facial animation is currently the most populartechnique utilized to generate realistic character facial animation forgames and movies. While effective, such techniques require specialequipment such as physical markers on a subject, structured light, andcamera arrays. As a result, such techniques are impractical for ordinaryusers.

Speech-driven facial animation is also a common technique, whichfunctions by first mapping raw speech features such as Mel-FrequencyCepstral Coefficients (MPCC) to predefined visual parameters. Thistechnique requires large volumes of corresponding audio and videotraining data for better generalized performance. The speech is mappedinto a phoneme or phoneme state feature and then to the visualparameters. While this method is easier to perform, accuracy dependsgreatly upon the volume of training data available.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a networkin accordance with some embodiments, wherein the messaging systemincludes a chat presence system.

FIG. 2 is block diagram illustrating further details regarding amessaging system, according to example embodiments.

FIG. 3 is a block diagram illustrating various modules of a chatpresence system, according to certain example embodiments.

FIG. 4 is a diagram illustrating various operations performed by aspeech recognition module, according to certain example embodiments.

FIG. 5 is a depiction of tracked facial landmarks, and correspondingfacial models, according to certain example embodiments.

FIG. 6 is a flowchart illustrating a method for generating an animatedfacial model based on audio and video data, according to certain exampleembodiments.

FIG. 7 is a flowchart illustrating a method for generating an animatedfacial model based on audio and video data, according to certain exampleembodiments.

FIG. 8 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described and used to implement variousembodiments.

FIG. 9 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The present invention relates to a joint automatic audio visual drivenfacial animation system that in some example embodiments includes a fullscale state of the art Large Vocabulary Continuous Speech Recognition(LVCSR) with a strong language model for speech recognition and obtainedphoneme alignment from the word lattice. Knowledge guided 3D blendshapes modeling is used for each phoneme, utilizing a 3D face modelingcapture device to avoid collecting training data as well as introducingbias from computer vision generated blend shapes. To further improvequality, the system may apply computer vision generated tracking, andjointly synthesize the facial animation by combining both audio andvideo information.

Joint video-speech driven facial animation systems, as discussed herein,combine the advantages of video and speech by using both video andacoustic inputs to track 3D facial motion. The system may apply a fullscale LVCSR model with a strong language model for word latticegeneration and phoneme sequence estimation. Thus, the disclosed systemsolves technical problems existing in current regression models thathave the disadvantages of requiring additional training data andincluding inherent inaccuracies from auto-generated facial regressiontarget parameters.

In some example embodiments, the Joint Audio-Visual Driven FacialAnimation System (Facial Animation System for short) includes a speechrecognition module. In some embodiments, a Bi-Directional LongShort-Term Memory (BLS™) or a Time Delayed Neural Network (TDNN) aretrained to convert input features to state posteriors. The inputfeatures may be obtained through passing framed raw audio signals (e.g.,audio data that includes human speech) through a Mel-Scaled filter bank.Unlike common cross entropy criterion, the disclosed training procedureis a lattice-free version of MMI, in which the denominator stateposteriors are obtained by the forward-backward algorithm on a hiddenMarkov model formed from a phone-level decoding graph. The numeratorstate posteriors are obtained by a similar forward-backward algorithm,limited to sequences corresponding to a transcript. For each outputindex of the neural net, a derivative of the difference between thenumerator and the denominator occupation probabilities is calculated andpropagated back to the network.

In some example embodiments, a trained acoustic model, a decision treefor mapping states to phones, a lexicon, and a pre-trained N-gramlanguage model are used to generate a weighted finite state transducer(WFST). The outputs of the WFST are transferred into a lattice, whereinthe most likely spoken word sequence is obtained through a Breadth-FirstSearch (BFS). The final phone sequence, as well as a start time and anend time of each phone may be inferred from the most probable wordsequence and lattice.

The Facial Animation System receives video data and tracks 2D faciallandmarks depicted in the video data, which locate face feature points'2D positions. Based on the tracked 2D landmarks, the Facial AnimationSystem reconstructs a 3D face model and then tracks movements of the 3Dface model.

2D facial landmarks correspond to semantic facial features of a humanface, such as the contour of eyes, lips and eyebrows, the tip of a nose,etc. Based on the tracked 2D facial landmarks, the system reconstructs auser's 3D face model. In some embodiments, the system determines anidentity of the user, wherein the identity defines the user's shapeunder neutral expression. With the identity, the system reconstructsuser-specific expression blend shapes, which describe the shape ofuser's different expressions.

For example, a sample set of neutral face shapes may be captured andreconstructed, such that any neutral face shape “F” may be representedby a linear combination of principal components.

${F = {\overset{\_}{A} + {\sum\limits_{i = 1}^{n}{\alpha_{i}A_{i}}}}},$

As seen in the above formula, “F” may be described as the reconstructedface shape, while Ā and {A_(i)} are the mean vector and PCA vectors of amorphable model, respectively, and a={a_(i)} are identity coefficients.

In some example embodiments, a first frame of a video is defined as aneutral expression of a user, with corresponding 2D facial landmarksvector P={p₁, p₂, . . . , p₆₈} (assuming 68 facial landmarks). In otherembodiments, any number of facial landmarks may be used. To match a 3Dface to the 2D facial landmarks, the system transforms the reconstructed3D face shape from object oriented to camera coordinate by applying arigid rotation and translation, and then projects the transformed faceshape into screen coordinates via the camera intrinsic matrix:

{circumflex over (F)}=Π(R·F+t),

Where F{circumflex over ( )} is the projected face shape, HO is theprojection operator using the camera intrinsic matric, which is definedby the camera, R and t are rigid rotation and translation respectively.To match F″ with tracked facial landmarks P, the system pre-defines thecorresponding vertex indices on 3D face shape (Green points in FIG. 2(b)). Indices of landmarks may be updated along the face contouraccording to a current projected face shape F. With the correspondingvertex indices {v₁, v₂, . . . , v₆₈), the system formulates the errorsmatching 3D face to the 2D landmarks as:

${E_{iden}^{f} = {\sum\limits_{k = 1}^{68}{{{\hat{F}}^{(v_{k})} - p_{k}}}^{2}}},$

Where F{circumflex over ( )} is the v_(k)-th vertex's position of faceshape F{circumflex over ( )}. The system regularizes the identitycoefficients a={a_(i)} based on an estimated probability distribution of3D morphable model's PCA.

With the reconstructed user-specific expression blend shapes, for eachinput frame at t, the system tracks the 3D face model parameterscombining input video and speech. These parameters may for exampleinclude, rigid rotation, translation, and non-rigid facial expressioncoefficients.

Landmark terms are used to describe the alignment between the tracked 3Dshape and 2D facial landmarks. The system reconstructs the expressionface shape. For example, similar to the identity determination process,the system applies the rigid rotation and translation to thereconstructed face shape, and projects the shape into screencoordinates. With the projected face shape F{circumflex over ( )}, thesystem may formulate the landmark term as:

${E^{f} = {\sum\limits_{k = 1}^{68}{{{\hat{F}}^{(v_{k})} - p_{k}}}^{2}}},$

Phoneme terms are used to describe the alignment between trackedexpression coefficients and estimates phonemes. The phoneme may beformulated as:

E ^(p) =∥b ^(t) −b _(p) ^(t)∥².

Smooth terms are used to enhance the smoothness of tracking results. Thesmooth term may be formulated as:

E ^(s) =∥b ^(t) −b ^(t-1)∥².

Putting these three terms together, we get a total energy function of:

E=E ^(f)+ω^(p) E ^(p)+ω^(s) E ^(s),

Where w_(p) and w_(s) balance the different terms. To optimize, thesystem applies similar coordinate-descent with the method in fittingidentity: in each iteration, the system firstly optimizes rigid rotationand translation while fixing expression coefficients; and vice versa. Inoptimizing the blend shape coefficients, different form fittingidentity, the system may constraint the range of each coefficient B_(i).The system may apply gradient projection algorithms based on the BFGSsolver to restrict the range of expression coefficients. The solvedrigid parameters and expression coefficients may thus be utilized togenerate the tracked 3D shape. In some example embodiments the 3D shapemay for example include a 3D avatar, or “3D bitmoji,” wherein the 3Dbitmoji or avatar are animated and presented based on the tracked 3Dshape derived from the input data that includes audio and video datareceived at a client device.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple client devices 102, each ofwhich hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality either within the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, but to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Insome embodiments, this data includes, message content, client deviceinformation, geolocation information, media annotation and overlays,message content persistence conditions, social network information, andlive event information, as examples. In other embodiments, other data isused. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via GUIs of the messaging clientapplication 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

Dealing specifically with the Application Program Interface (API) server110, this server receives and transmits message data (e.g., commands andmessage payloads) between the client device 102 and the applicationserver 112. Specifically, the Application Program Interface (API) server110 provides a set of interfaces (e.g., routines and protocols) that canbe called or queried by the messaging client application 104 in order toinvoke functionality of the application server 112. The ApplicationProgram Interface (API) server 110 exposes various functions supportedby the application server 112, including account registration, loginfunctionality, the sending of messages, via the application server 112,from a particular messaging client application 104 to another messagingclient application 104, the sending of media files (e.g., images orvideo) from a messaging client application 104 to the messaging serverapplication 114, and for possible access by another messaging clientapplication 104, the setting of a collection of media data (e.g.,story), the retrieval of a list of friends of a user of a client device102, the retrieval of such collections, the retrieval of messages andcontent, the adding and deletion of friends to a social graph, thelocation of friends within a social graph, opening and application event(e.g., relating to the messaging client application 104).

The application server 112 hosts a number of applications andsubsystems, including a messaging server application 114, an imageprocessing system 116, a social network system 122, and a facialanimation system 124. The messaging server application 114 implements anumber of message processing technologies and functions, particularlyrelated to the aggregation and other processing of content (e.g.,textual and multimedia content) included in messages received frommultiple instances of the messaging client application 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available, by themessaging server application 114, to the messaging client application104. Other processor and memory intensive processing of data may also beperformed server-side by the messaging server application 114, in viewof the hardware requirements for such processing.

The application server 112 also includes an image processing system 116that is dedicated to performing various image processing operations,typically with respect to images or video received within the payload ofa message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions services, and makes these functions and services available tothe messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph 304 within thedatabase 120. Examples of functions and services supported by the socialnetwork system 122 include the identification of other users of themessaging system 100 with which a particular user has relationships oris “following,” and also the identification of other entities andinterests of a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of some subsystems, namely an ephemeral timer system 202, acollection management system 204 and an annotation system 206.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message,collection of messages (e.g., a SNAPCHAT story), or graphical element,selectively display and enable access to messages and associated contentvia the messaging client application 104. Further details regarding theoperation of the ephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image video and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text and audio) may be organized into an “eventgallery” or an “event story.” Such a collection may be made availablefor a specified time period, such as the duration of an event to whichthe content relates. For example, content relating to a music concertmay be made available as a “story” for the duration of that musicconcert. The collection management system 204 may also be responsiblefor publishing an icon that provides notification of the existence of aparticular collection to the user interface of the messaging clientapplication 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion of usergenerated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a SNAPCHAT filter) to themessaging client application 104 based on a geolocation of the clientdevice 102. In another example, the annotation system 206 operativelysupplies a media overlay to the messaging client application 104 basedon other information, such as, social network information of the user ofthe client device 102. A media overlay may include audio and visualcontent and visual effects. Examples of audio and visual content includepictures, texts, logos, animations, and sound effects, as well asanimated facial models, such as those generated by the facial animationsystem 124. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay including text that can be overlaid on top of a photographgenerated taken by the client device 102. In another example, the mediaoverlay includes an identification of a location overlay (e.g., Venicebeach), a name of a live event, or a name of a merchant overlay (e.g.,Beach Coffee House). In another example, the annotation system 206 usesthe geolocation of the client device 102 to identify a media overlaythat includes the name of a merchant at the geolocation of the clientdevice 102. The media overlay may include other indicia associated withthe merchant. The media overlays may be stored in the database 120 andaccessed through the database server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map, and upload content associated with the selectedgeolocation. The user may also specify circumstances under which aparticular media overlay should be offered to other users. Theannotation system 206 generates a media overlay that includes theuploaded content and associates the uploaded content with the selectedgeolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest bidding merchant with a corresponding geolocationfor a predefined amount of time

FIG. 3 is a block diagram illustrating components of the facialanimation system 124 that configure the facial animation system 124 toreceive audio and video data, and generate a 3D facial model based on acombination of the audio and video data, according to some exampleembodiments. The facial animation system 124 is shown as including aspeech recognition module 302, a video module 304, a modeling module306, and presentation module 308, all configured to communicate witheach other (e.g., via a bus, shared memory, or a switch). Any one ormore of these modules may be implemented using one or more processors310 (e.g., by configuring such one or more processors to performfunctions described for that module) and hence may include one or moreof the processors 310.

Any one or more of the modules described may be implemented usinghardware alone (e.g., one or more of the processors 310 of a machine) ora combination of hardware and software. For example, any moduledescribed of the facial animation system 124 may physically include anarrangement of one or more of the processors 310 (e.g., a subset of oramong the one or more processors of the machine) configured to performthe operations described herein for that module. As another example, anymodule of the facial animation system 124 may include software,hardware, or both, that configure an arrangement of one or moreprocessors 310 (e.g., among the one or more processors of the machine)to perform the operations described herein for that module. Accordingly,different modules of the facial animation system 124 may include andconfigure different arrangements of such processors 310 or a singlearrangement of such processors 310 at different points in time.Moreover, any two or more modules of the facial animation system 124 maybe combined into a single module, and the functions described herein fora single module may be subdivided among multiple modules. Furthermore,according to various example embodiments, modules described herein asbeing implemented within a single machine, database, or device may bedistributed across multiple machines, databases, or devices.

FIG. 4 is a diagram 400 illustrating various operations performed by aspeech recognition module (e.g., speech recognition module 302),according to certain example embodiments.

As seen in the diagram 400, a neural network 402, such as aBi-directional Long Short-Term Memory (BLSTM 404) or a Time delayedNeural Network (TDNN 406), is trained to convert input features (e.g.,speech features 408) into state posteriors. In some embodiments, thespeech features are obtained by passing framed raw audio signals througha Mel frequency scaled filter bank. Unlike cross entropy criterion, thetraining procedure is a lattice-free version of MMI, in which thedenominator state posteriors may be obtained by the forward-backwardalgorithm on an hidden Markov model formed from a phone-level decodinggraph, and the numerator state posteriors are obtained by a similarforward-backward algorithm, but limited to sequences corresponding tothe transcript.

For each output index of the neural network 402, a derivative of thedifference between numerator and denominator occupation probabilities iscalculated and propagated back to the neural network 402.

In the inferring stage, as illustrated in the diagram 400, the trainedacoustic model (e.g., trained acoustic model 410A or 410B), a decisiontree for mapping the states to phones (e.g., word lattice 416), alexicon 414, and a pre-trained N-gram language model 412, are used togenerate a weight finite state transducer (e.g., phoneme sequence 418).

From the word lattice 416, the most likely spoken word sequence may beobtained via Breadth First Search (BFS). The final phone sequence, aswell as a start time and an end time of each phone may be inferred fromthe most probable word sequence and word lattice 416.

FIG. 5 is a depiction of tracked facial landmarks 502 and 508, andcorresponding facial models 510 and 512, according to certain exampleembodiments. As seen in FIG. 5, the tracked facial landmarks 502 and 508comprises a set of points, wherein each point corresponds to a faciallandmark identified by the facial animation system 124, based on videodata 504 and 506, respectively.

The facial animation system 124 receives video data 504 and 506, andtracks the facial landmarks 502 and 508, wherein the facial landmarks502 and 508 correspond to the semantic facial features of a human face,such as the contour of eyes, lips, nose, and eyebrows. The video data504 and 506 comprises a video component and an audio component. In someembodiments, the facial animation system 124 may parse the video data504 and 506 to process the audio data and video data separate from oneanother.

The facial animation system 124 constructs the facial models 510 and 512based on the tracked facial landmarks 502 and 508, and the audiocomponents of the video data 504 and 506. In some embodiments, thesystem determines an identity of the user, wherein the identity isdetermined based on one or more of the facial landmarks of the user'sface, or based on an expression of the user, wherein the expression isbased on an orientation of the facial landmarks.

FIG. 6 is a flowchart illustrating a method 600 for generating ananimated facial model, as depicted in FIG. 5, based on audio and videodata, according to certain example embodiments. Operations of the method600 may be performed by the modules described above with respect to FIG.3. As shown in FIG. 6, the method 600 includes one or more operations602, 604, 606, 608, 610, 612, and 614.

At operation 602, the video module 304 receives audio data and videodata at a client device (e.g., client device 102 of FIG. 1). Forexample, the client device 102 may record or otherwise capture a videocomprising an audio component and a video component. The video module304 may separate the audio component and the video component form thevideo, in order to process the components separately.

At operation 604, the speech recognition module 302 determines a phonesequence of the audio data (the audio component of the recorded video).The audio data may for example include a speech signal comprisingphonemes, wherein a phoneme is a unit of speech that differentiates oneword from another in the speech signal. For example, one phoneme mayconsist of a sequence of closure, burst, and aspiration events; or, adipthong may transition from a back vowel to a front vowel. A speechsignal may therefore be described not only by what phonemes it contains,but also a sequence, or alignment of the phonemes. Phoneme alignment maytherefore be described as a “time-alignment” of phonemes in a waveform,in order to determine an appropriate sequence and location of eachphoneme in a speech signal.

In some embodiments, the speech recognition module 302 may perform fullscale Large Vocabulary Continuous Speech Recognition (LVCSR), with astrong language model for word lattice generation and phoneme sequenceestimation in order to determine the sequence of the phonemes in thespeech signal.

At operation 606, the video module 304 determines locations of a set offacial landmarks based on the video component of the video data. In someexample embodiments, facial landmarks detection may include algorithmsto perform facial alignment with an ensemble of regression trees. Faciallandmarks correspond to semantic facial features of a human face, suchas the contour of eyes, lips and eyebrows, the tip of a nose, etc. Insome embodiments, the video module 304 determines an identity of a userdepicted in the video data, wherein the identity is based on anorientation of the identified facial landmarks. In further embodiments,the video module 304 may access user profile data based on the identityof the user, wherein the user profile data includes a set of displayspecifications that define how a facial model is to be presented. Forexample, the user profile may indicate specific colors, interfaceelements, or a selection of a specific bitmoji (e.g., avatar).

At operations 608 and 610, the modeling module 306 generates a firstfacial model based on the tracked facial landmarks, and a second facialmodel based on the phone sequence of the audio data, and at operation612, the modeling module 306 generates a composite facial model (e.g.,facial model 510 and 512) based on the first facial model and the secondfacial model. In some embodiments, the composite facial model may alsobe based on the display specifications from the user profile of theidentified user.

At operation 614, the presentation module 308 generates and causesdisplay of a presentation of the composite facial model at a clientdevice (e.g., client device 102). In some embodiments, the presentationmodule 308 may display the presentation of the composite facial modelwithin a graphical user interface. In further embodiments, thepresentation module 308 may stylize or alter the composite facial modelwith the addition of graphical elements, or filters which alter thedisplay of the composite facial model.

In some example embodiments, the method 600 is performed in real-time,as video data is collected at the client device 102. For example, a userof the client device 102 may collect or otherwise record the video dataat the client device 102 (or through a remote device), and the facialanimation system 124 may receive and process the video data based on themethod 500, to display the composite facial model at a client device 102(e.g., a device of another user).

In some embodiments, the composite facial model may be presented withinan ephemeral message delivered from a first client device to a secondclient device. For example, a user of the client device 102 may record amessage that include video data, and the facial animation system 124 mayprocess the video data based on the method 500, in order to generate andpresent an ephemeral message that includes a presentation of thecomposite facial model.

FIG. 7 is a flowchart illustrating a method 700 for generating ananimated facial model, as depicted in FIG. 5, based on audio and videodata, according to certain example embodiments. Operations of the method700 may be performed by the modules described above with respect to FIG.3. As shown in FIG. 7, the method 700 includes one or more operations702, 704, 706, 708, and 710.

At operation 702, the modeling module 306 detects a loss of audio data,video data, or both. For example, the audio or video received at theclient device 102 may be corrupted or obscured, resulting in anincomplete or fragmented facial model.

At operation 704, the video module 304 parses the video data to identifya first frame. For example, the first frame may indicate a startingpoint of the video data.

At operation 706, the video module 304 determines locations of the setof facial landmarks within the first frame of the video data, and atoperation 708, defines the locations of the facial landmarks in thefirst frame as a “neutral expression.”

At operation 710, in response to the detecting the loss of the audio andvideo data, the presentation module 308 alters a presentation of thecomposite facial model based on the locations of the set of faciallandmarks from the neutral expression.

Software Architecture

FIG. 8 is a block diagram illustrating an example software architecture806, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 8 is a non-limiting example of asoftware architecture and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 806 may execute on hardwaresuch as machine 900 of FIG. 9 that includes, among other things,processors 904, memory 914, and I/O components 918. A representativehardware layer 852 is illustrated and can represent, for example, themachine 800 of FIG. 8. The representative hardware layer 852 includes aprocessing unit 854 having associated executable instructions 804.Executable instructions 804 represent the executable instructions of thesoftware architecture 806, including implementation of the methods,components and so forth described herein. The hardware layer 852 alsoincludes memory and/or storage modules memory/storage 856, which alsohave executable instructions 804. The hardware layer 852 may alsocomprise other hardware 858.

In the example architecture of FIG. 8, the software architecture 806 maybe conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 806 mayinclude layers such as an operating system 802, libraries 820,applications 816 and a presentation layer 814. Operationally, theapplications 816 and/or other components within the layers may invokeapplication programming interface (API) API calls 808 through thesoftware stack and receive a response as in response to the API calls808. The layers illustrated are representative in nature and not allsoftware architectures have all layers. For example, some mobile orspecial purpose operating systems may not provide aframeworks/middleware 818, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 802 may manage hardware resources and providecommon services. The operating system 802 may include, for example, akernel 822, services 824 and drivers 826. The kernel 822 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 822 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 824 may provideother common services for the other software layers. The drivers 826 areresponsible for controlling or interfacing with the underlying hardware.For instance, the drivers 826 include display drivers, camera drivers,Bluetooth® drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audiodrivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 820 provide a common infrastructure that is used by theapplications 816 and/or other components and/or layers. The libraries820 provide functionality that allows other software components toperform tasks in an easier fashion than to interface directly with theunderlying operating system 802 functionality (e.g., kernel 822,services 824 and/or drivers 826). The libraries 820 may include systemlibraries 844 (e.g., C standard library) that may provide functions suchas memory allocation functions, string manipulation functions,mathematical functions, and the like. In addition, the libraries 820 mayinclude API libraries 846 such as media libraries (e.g., libraries tosupport presentation and manipulation of various media format such asMPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., anOpenGL framework that may be used to render 2D and 3D in a graphiccontent on a display), database libraries (e.g., SQLite that may providevarious relational database functions), web libraries (e.g., WebKit thatmay provide web browsing functionality), and the like. The libraries 820may also include a wide variety of other libraries 848 to provide manyother APIs to the applications 816 and other softwarecomponents/modules.

The frameworks/middleware 818 (also sometimes referred to as middleware)provide a higher-level common infrastructure that may be used by theapplications 816 and/or other software components/modules. For example,the frameworks/middleware 818 may provide various graphic user interface(GUI) functions, high-level resource management, high-level locationservices, and so forth. The frameworks/middleware 818 may provide abroad spectrum of other APIs that may be utilized by the applications816 and/or other software components/modules, some of which may bespecific to a particular operating system 802 or platform.

The applications 816 include built-in applications 838 and/orthird-party applications 840. Examples of representative built-inapplications 838 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. Third-party applications 840 may include anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 840 may invoke the API calls 808 provided bythe mobile operating system (such as operating system 802) to facilitatefunctionality described herein.

The applications 816 may use built in operating system functions (e.g.,kernel 822, services 824 and/or drivers 826), libraries 820, andframeworks/middleware 818 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systemsinteractions with a user may occur through a presentation layer, such aspresentation layer 814. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 9 is a block diagram illustrating components of a machine 900,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 9 shows a diagrammatic representation of the machine900 in the example form of a computer system, within which instructions910 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 900 to perform any one ormore of the methodologies discussed herein may be executed. As such, theinstructions 910 may be used to implement modules or componentsdescribed herein. The instructions 910 transform the general,non-programmed machine 900 into a particular machine 900 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 900 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 900 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 910, sequentially or otherwise, that specify actions to betaken by machine 900. Further, while only a single machine 900 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 910 to perform any one or more of the methodologiesdiscussed herein.

The machine 900 may include processors 904, memory memory/storage 906,and I/O components 918, which may be configured to communicate with eachother such as via a bus 902. The memory/storage 906 may include a memory914, such as a main memory, or other memory storage, and a storage unit916, both accessible to the processors 904 such as via the bus 902. Thestorage unit 916 and memory 914 store the instructions 910 embodying anyone or more of the methodologies or functions described herein. Theinstructions 910 may also reside, completely or partially, within thememory 914, within the storage unit 916, within at least one of theprocessors 904 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine900. Accordingly, the memory 914, the storage unit 916, and the memoryof processors 904 are examples of machine-readable media.

The I/O components 918 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 918 that are included in a particular machine 900 will dependon the type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components 918may include many other components that are not shown in FIG. 9. The I/Ocomponents 918 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 918 mayinclude output components 926 and input components 928. The outputcomponents 926 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 928 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 918 may includebiometric components 930, motion components 934, environmentalenvironment components 936, or position components 938 among a widearray of other components. For example, the biometric components 930 mayinclude components to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 934 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 936 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 938 mayinclude location sensor components (e.g., a Global Position system (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 918 may include communication components 940 operableto couple the machine 900 to a network 932 or devices 920 via coupling922 and coupling 924 respectively. For example, the communicationcomponents 940 may include a network interface component or othersuitable device to interface with the network 932. In further examples,communication components 940 may include wired communication components,wireless communication components, cellular communication components,Near Field Communication (NFC) components, Bluetooth® components (e.g.,Bluetooth® Low Energy), Wi-Fi® components, and other communicationcomponents to provide communication via other modalities. The devices920 may be another machine or any of a wide variety of peripheraldevices (e.g., a peripheral device coupled via a Universal Serial Bus(USB)).

Moreover, the communication components 940 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 940 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components940, such as, location via Internet Protocol (IP) geo-location, locationvia Wi-Fi® signal triangulation, location via detecting a NFC beaconsignal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine, and includes digital or analog communications signals orother intangible medium to facilitate communication of suchinstructions. Instructions may be transmitted or received over thenetwork using a transmission medium via a network interface device andusing any one of a number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smart phones, tablets, ultra books, netbooks,laptops, multi-processor systems, microprocessor-based or programmableconsumer electronics, game consoles, set-top boxes, or any othercommunication device that a user may use to access a network.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or other typeof cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard setting organizations,other long range protocols, or other data transfer technology.

“EMPHEMERAL MESSAGE” in this context refers to a message that isaccessible for a time-limited duration. An ephemeral message may be atext, an image, a video and the like. The access time for the ephemeralmessage may be set by the message sender. Alternatively, the access timemay be a default setting or a setting specified by the recipient.Regardless of the setting technique, the message is transitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, deviceor other tangible media able to store instructions and data temporarilyor permanently and may include, but is not be limited to, random-accessmemory (RAM), read-only memory (ROM), buffer memory, flash memory,optical media, magnetic media, cache memory, other types of storage(e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or anysuitable combination thereof. The term “machine-readable medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-readablemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-readable medium”excludes signals per se.

“COMPONENT” in this context refers to a device, physical entity or logichaving boundaries defined by function or subroutine calls, branchpoints, application program interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components. A “hardware component” is a tangibleunit capable of performing certain operations and may be configured orarranged in a certain physical manner. In various example embodiments,one or more computer systems (e.g., a standalone computer system, aclient computer system, or a server computer system) or one or morehardware components of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a Field-Programmable Gate Array (FPGA) or an ApplicationSpecific Integrated Circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)). The performance of certain of the operations may bedistributed among the processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processors or processor-implemented components may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands”, “op codes”, “machine code”, etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an Application SpecificIntegrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC)or any combination thereof. A processor may further be a multi-coreprocessor having two or more independent processors (sometimes referredto as “cores”) that may execute instructions contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

“LIFT” in this context is a measure of the performance of a targetedmodel at predicting or classifying cases as having an enhanced response(with respect to a population as a whole), measured against a randomchoice targeting model.

“PHONEME ALIGNMENT” in this context, a phoneme is a unit of speech thatdifferentiates one word from another. One phoneme may consist of asequence of closure, burst, and aspiration events; or, a dipthong maytransition from a back vowel to a front vowel. A speech signal maytherefore be described not only by what phonemes it contains, but alsothe locations of the phonemes. Phoneme alignment may therefore bedescribed as a “time-alignment” of phonemes in a waveform, in order todetermine an appropriate sequence and location of each phoneme in aspeech signal.

“AUDIO-TO-VISUAL CONVERSION” in this context refers to the conversion ofaudible speech signals into visible speech, wherein the visible speechmay include a mouth shape representative of the audible speech signal.

“TIME DELAYED NEURAL NETWORK (TDNN)” in this context, a TDNN is anartificial neural network architecture whose primary purpose is to workon sequential data. An example would be converting continuous audio intoa stream of classified phoneme labels for speech recognition.

“BI-DIRECTIONAL LONG-SHORT TERM MEMORY (BLS™)” in this context refers toa recurrent neural network (RNN) architecture that remembers values overarbitrary intervals. Stored values are not modified as learningproceeds. RNNs allow forward and backward connections between neurons.BLS™ are well-suited for the classification, processing, and predictionof time series, given time lags of unknown size and duration betweenevents.

What is claimed is:
 1. A method comprising: accessing a data stream thatcomprises audio data and video data at a client device; identifying auser profile that corresponds with the set of facial landmarks from thevideo data of the data stream, the user profile comprising user profiledata; generating a facial model based on the user profile data; causingdisplay of a presentation of the facial model; animating thepresentation of the facial model based on the audio data; detecting aloss in the audio data of the data stream; accessing a first frame ofthe video data in response to the loss in the data stream; and causingdisplay of the presentation of the facial model based on at least thefirst frame.
 2. The method of claim 1, wherein the generating the facialmodel includes: generating a first facial model based on the audio data;generating a second facial model based on the video data; and generatinga composite facial model based on the first facial model and the secondfacial model.
 3. The method of claim 1, wherein the facial modelincludes a three-dimensional (3D) facial model.
 4. The method of claim1, wherein the client device is a first client device, and the causingdisplay of the facial model includes: causing display of the facialmodel at a second client device.
 5. The method of claim 1, wherein theuser profile data includes a selection of a user avatar, and wherein thegenerating the facial model includes: generating a facial model based onthe selection of the user avatar.
 6. The method of claim 1, wherein theanimating the presentation of the facial model based on the audio dataincludes: determining a phone sequence based on the audio data.
 7. Themethod of claim 1, wherein the causing display of the presentation ofthe facial model includes: causing display of an ephemeral message thatincludes the presentation of the facial model.
 8. A system comprising: amemory; and at least one hardware processor coupled to the memory andcomprising instructions that causes the system to perform operationscomprising: accessing a data stream that comprises audio data and videodata at a client device; identifying a user profile that correspondswith the set of facial landmarks from the video data of the data stream,the user profile comprising user profile data; generating a facial modelbased on the user profile data; causing display of a presentation of thefacial model; animating the presentation of the facial model based onthe audio data; detecting a loss in the audio data of the data stream;accessing a first frame of the video data in response to the loss in thedata stream; and causing display of the presentation of the facial modelbased on at least the first frame.
 9. The system of claim 8, wherein thegenerating the facial model includes: generating a first facial modelbased on the audio data; generating a second facial model based on thevideo data; and generating a composite facial model based on the firstfacial model and the second facial model.
 10. The system of claim 8,wherein the facial model includes a three-dimensional (3D) facial model.11. The system of claim 8, wherein the client device is a first clientdevice, and the causing display of the facial model includes: causingdisplay of the facial model at a second client device.
 12. The system ofclaim 8, wherein the user profile data includes a selection of a useravatar, and wherein the generating the facial model includes: generatinga facial model based on the selection of the user avatar.
 13. The systemof claim 8, wherein the animating the presentation of the facial modelbased on the audio data includes: determining a phone sequence based onthe audio data.
 14. The system of claim 8, wherein the causing displayof the presentation of the facial model includes: causing display of anephemeral message that includes the presentation of the facial model.15. A non-transitory machine-readable storage medium comprisinginstructions that, when executed by one or more processors of a machine,cause the machine to perform operations comprising: accessing a datastream that comprises audio data and video data at a client device;identifying a user profile that corresponds with the set of faciallandmarks from the video data of the data stream, the user profilecomprising user profile data; generating a facial model based on theuser profile data; causing display of a presentation of the facialmodel; animating the presentation of the facial model based on the audiodata; detecting a loss in the audio data of the data stream; accessing afirst frame of the video data in response to the loss in the datastream; and causing display of the presentation of the facial modelbased on at least the first frame.
 16. The non-transitorymachine-readable storage medium of claim 15, wherein the generating thefacial model includes: generating a first facial model based on theaudio data; generating a second facial model based on the video data;and generating a composite facial model based on the first facial modeland the second facial model.
 17. The non-transitory machine-readablestorage medium of claim 15, wherein the facial model includes athree-dimensional (3D) facial model.
 18. The non-transitorymachine-readable storage medium of claim 15, wherein the client deviceis a first client device, and the causing display of the facial modelincludes: causing display of the facial model at a second client device.19. The non-transitory machine-readable storage medium of claim 15,wherein the user profile data includes a selection of a user avatar, andwherein the generating the facial model includes: generating a facialmodel based on the selection of the user avatar.
 20. The non-transitorymachine-readable storage medium of claim 15, wherein the animating thepresentation of the facial model based on the audio data includes:determining a phone sequence based on the audio data.